In various imaging systems, due to factors such as lack of focus, a relative motion between the system and an object, a cross-crosstalk of a detector and the like, images may be blurred, thereby degrading an image quality of the images, which may further affect visual effects and subsequent feature extraction and identification of the images. An image ambiguity can be quantitatively estimated by evaluation algorithms. The evaluation algorithms can be used in image quality monitoring and auto focusing of the imaging systems, and can further improve effects of subsequent image processing algorithms.
The methods for evaluating the image quality may comprise image quality evaluating methods with references, with semi-references, or with no-reference. In consideration of the fact that a non-degraded clear image can not be obtained in practical application, a method of evaluating an image ambiguity with no reference is more practical. The no-reference image ambiguity evaluation method may include an edge-based method, a pixel-based statistical information method, a transform domain-based method, and so on. The methods for evaluating the image ambiguity based on edge analysis may have an advantage of its concept being intuitive and its calculation being relatively simple. However, the method has a certain dependence on the image. When there is no sharp edge in an original image, the method becomes inaccurate. The methods for evaluating the image ambiguity based on the ambiguity of pixels may utilize statistical information of the image and thus may have a good robustness. However, the method ignores position information of the pixels, and the noise (especially, impulse noise) in the image will form a strong gradient value, which will affect the evaluation greatly. The methods for evaluating the image ambiguity based on transform domain take frequency domain characteristics and multi-scale characteristics into account. Some of the methods even utilize spatial information, thus, may have a good accuracy and robustness. However, neither of the methods does use a characteristic of the consistency among image information on different scales, and many methods still need to train a regression function, which may cause inconveniences to practical usage.